A Comprehensive Guide to Quality Assessment and Data Submission for Genomic Surveillance of Enteric Pathogens.

This document outlines the steps necessary to assemble and submit the standard data package required for contributing to the global genomic surveillance of enteric pathogens. Although targeted to GenomeTrakr laboratories and collaborators, these protocols are broadly applicable for enteric pathogens collected for different purposes. There are five protocols included in this chapter: (1) quality control (QC) assessment for the genome sequence data, (2) validation for the contextual data, (3) data submission for the standard pathogen package or Pathogen Data Object Model (DOM) to the public repository, (4) viewing and querying data at NCBI, and (5) data curation for maintaining relevance of public data. The data are available through one of the International Nucleotide Sequence Database Consortium (INSDC) members, with the National Center for Biotechnology Information (NCBI) being the primary focus of this document. NCBI Pathogen Detection is a custom dashboard at NCBI that provides easy access to pathogen data plus results for a standard suite of automated cluster and genotyping analyses important for informing public health and regulatory decision-making.

Genomic analysis of Vibrio cholerae O1 isolates from cholera cases, Europe, 2022.

BackgroundThe number of cholera cases reported to the World Health Organization (WHO) in 2022 was more than double that of 2021. Nine countries of the WHO European Region reported 51 cases of cholera in 2022 vs five reported cases in 2021.AimWe aimed to confirm that the Vibrio cholerae O1 isolates reported by WHO European Region countries in 2022 belonged to the seventh pandemic El Tor lineage (7PET). We also studied their virulence, antimicrobial resistance (AMR) determinants and phylogenetic relationships.MethodsWe used microbial genomics to study the 49 V. cholerae O1 isolates recovered from the 51 European cases. We also used > 1,450 publicly available 7PET genomes to provide a global phylogenetic context for these 49 isolates.ResultsAll 46 good-quality genomes obtained belonged to the 7PET lineage. All but two isolates belonged to genomic Wave 3 and were grouped within three sub-lineages, one of which, Pre-AFR15, predominated (34/44). This sub-lineage, corresponding to isolates from several countries in Southern Asia, the Middle East and Eastern or Southern Africa, was probably a major contributor to the global upsurge of cholera cases in 2022. No unusual AMR profiles were inferred from analysis of the AMR gene content of the 46 genomes.ConclusionReference laboratories in high-income countries should use whole genome sequencing to assign V. cholerae O1 isolates formally to the 7PET or non-epidemic lineages. Periodic collaborative genomic studies based on isolates from travellers can provide useful information on the circulating strains and their evolution, particularly as concerns AMR.

The protein domains of vertebrate species in which selection is more effective have greater intrinsic structural disorder.

The nearly neutral theory of molecular evolution posits variation among species in the effectiveness of selection. In an idealized model, the census population size determines both this minimum magnitude of the selection coefficient required for deleterious variants to be reliably purged, and the amount of neutral diversity. Empirically, an 'effective population size' is often estimated from the amount of putatively neutral genetic diversity and is assumed to also capture a species' effectiveness of selection. A potentially more direct measure of the effectiveness of selection is the degree to which selection maintains preferred codons. However, past metrics that compare codon bias across species are confounded by among-species variation in %GC content and/or amino acid composition. Here, we propose a new Codon Adaptation Index of Species (CAIS), based on Kullback-Leibler divergence, that corrects for both confounders. We demonstrate the use of CAIS correlations, as well as the Effective Number of Codons, to show that the protein domains of more highly adapted vertebrate species evolve higher intrinsic structural disorder.

Evolution is the process through which populations change over time, starting with mutations in the genetic sequence of an organism. Many of these mutations harm the survival and reproduction of an organism, but only by a very small amount. Some species, especially those with large populations, can purge these slightly harmful mutations more effectively than other species. This fact has been used by the 'drift barrier theory' to explain various profound differences amongst species, including differences in biological complexity. In this theory, the effectiveness of eliminating slightly harmful mutations is specified by an 'effective' population size, which depends on factors beyond just the number of individuals in the population. Effective population size is normally calculated from the amount of time a 'neutral' mutation (one with no effect at all) stays in the population before becoming lost or taking over. Estimating this time requires both representative data for genetic diversity and knowledge of the mutation rate. A major limitation is that these data are unavailable for most species. A second limitation is that a brief, temporary reduction in the number of individuals has an oversized impact on the metric, relative to its impact on the number of slighly harmful mutations accumulated. Weibel, Wheeler et al. developed a new metric to more directly determine how effectively a species purges slightly harmful mutations. Their approach is based on the fact that the genetic code has 'synonymous' sequences. These sequences code for the same amino acid building block, with one of these sequences being only slightly preferred over others. The metric by Weibel, Wheeler et al. quantifies the proportion of the genome from which less preferred synonymous sequences have been effectively purged. It judges a population to have a higher effective population size when the usage of synonymous sequences departs further from the usage predicted from mutational processes. The researchers expected that natural selection would favour 'ordered' proteins with robust three-dimensional structures, i.e., that species with a higher effective population size would tend to have more ordered versions of a protein. Instead, they found the opposite: species with a higher effective population size tend to have more disordered versions of the same protein. This changes our view of how natural selection acts on proteins. Why species are so different remains a fundamental question in biology. Weibel, Wheeler et al. provide a useful tool for future applications of drift barrier theory to a broad range of ways that species differ.

Early moderate prenatal alcohol exposure and maternal diet impact offspring DNA methylation across species.

Alcohol consumption in pregnancy can affect genome regulation in the developing offspring but results have been contradictory. We employed a physiologically relevant murine model of short-term moderate prenatal alcohol exposure (PAE) resembling common patterns of alcohol consumption in pregnancy in humans. Early moderate PAE was sufficient to affect site-specific DNA methylation in newborn pups without altering behavioural outcomes in adult littermates. Whole-genome bisulfite sequencing of neonatal brain and liver revealed stochastic influence on DNA methylation that was mostly tissue-specific, with some perturbations likely originating as early as gastrulation. DNA methylation differences were enriched in non-coding genomic regions with regulatory potential indicative of broad effects of alcohol on genome regulation. Replication studies in human cohorts with fetal alcohol spectrum disorder suggested some effects were metastable at genes linked to disease-relevant traits including facial morphology, intelligence, educational attainment, autism, and schizophrenia. In our murine model, a maternal diet high in folate and choline protected against some of the damaging effects of early moderate PAE on DNA methylation. Our studies demonstrate that early moderate exposure is sufficient to affect fetal genome regulation even in the absence of overt phenotypic changes and highlight a role for preventative maternal dietary interventions.

Drinking excessive amounts of alcohol during pregnancy can cause foetal alcohol spectrum disorder and other conditions in children that affect their physical and mental development. Many countries advise women who are pregnant or trying to conceive to avoid drinking alcohol entirely. However, surveys of large groups of women in Western countries indicate that most women continue drinking low to moderate amounts of alcohol until they discover they are pregnant and then stop consuming alcohol for the rest of their pregnancy. It remains unclear how this common drinking pattern affects the foetus. The instructions needed to build and maintain a human body are stored within molecules of DNA. Some regions of DNA called genes contain the instructions to make proteins, which perform many tasks in the body. Other so-called 'non-coding' regions do not code for any proteins but instead have roles in regulating gene activity. One way cells control which genes are switched on or off is adding or removing tags known as methyl groups to certain locations on DNA. Previous studies indicate that alcohol may affect how children develop by changing the patterns of methyl tags on DNA. To investigate the effect of moderate drinking during the early stages of pregnancy, Bestry et al. exposed pregnant mice to alcohol and examined how this affected the patterns of methyl tags on DNA in their offspring. The experiments found moderate levels of alcohol were sufficient to alter the patterns of methyl tags in the brains and livers of the newborn mice. Most of the changes were observed in non-coding regions of DNA, suggesting alcohol may affect how large groups of genes are regulated. Fewer changes in the patterns of methyl tags were found in mice whose mothers had diets rich in two essential nutrients known as folate and choline. Further experiments found that some of the affected mouse genes were similar to genes linked to foetal alcohol spectrum disorder and other related conditions in humans. These findings highlight the potential risks of consuming even moderate levels of alcohol during pregnancy and suggest that a maternal diet rich in folate and choline may help mitigate some of the harmful effects on the developing foetus.

Web of venom: exploration of big data resources in animal toxin research.

Research on animal venoms and their components spans multiple disciplines, including biology, biochemistry, bioinformatics, pharmacology, medicine, and more. Manipulating and analyzing the diverse array of data required for venom research can be challenging, and relevant tools and resources are often dispersed across different online platforms, making them less accessible to nonexperts. In this article, we address the multifaceted needs of the scientific community involved in venom and toxin-related research by identifying and discussing web resources, databases, and tools commonly used in this field. We have compiled these resources into a comprehensive table available on the VenomZone website (https://venomzone.expasy.org/10897). Furthermore, we highlight the challenges currently faced by researchers in accessing and using these resources and emphasize the importance of community-driven interdisciplinary approaches. We conclude by underscoring the significance of enhancing standards, promoting interoperability, and encouraging data and method sharing within the venom research community.

Disentangling river and swamp buffalo genetic diversity: initial insights from the 1000 Buffalo Genomes Project.

More people in the world depend on water buffalo for their livelihoods than on any other domesticated animals, but its genetics is still not extensively explored. The 1000 Buffalo Genomes Project (1000BGP) provides genetic resources for global buffalo population study and tools to breed more sustainable and productive buffaloes. Here we report the most contiguous swamp buffalo genome assembly (PCC_UOA_SB_1v2) with substantial resolution of telomeric and centromeric repeats, ∼4-fold more contiguous than the existing reference river buffalo assembly and exceeding a recently published male swamp buffalo genome. This assembly was used along with the current reference to align 140 water buffalo short-read sequences and produce a public genetic resource with an average of ∼41 million single nucleotide polymorphisms per swamp and river buffalo genome. Comparison of the swamp and river buffalo sequences showed ∼1.5% genetic differences, and estimated divergence time occurred 3.1 million years ago (95% CI, 2.6-4.9). The open science model employed in the 1000BGP provides a key genomic resource and tools for a species with global economic relevance.

Project GIVE: using a virtual genetics service platform to reduce health inequities and improve access to genomic care in an underserved region of Texas.

BACKGROUND: The utilization of genomic information to improve health outcomes is progressively becoming more common in clinical practice. Nonetheless, disparities persist in accessing genetic services among ethnic minorities, individuals with low socioeconomic status, and other vulnerable populations. The Rio Grande Valley (RGV) at the Texas-Mexico border is predominantly Hispanic/Latino with a high poverty rate and very limited access to genetic services. Funded by the National Center for Advancing Translational Sciences, Project GIVE (Genetic Inclusion by Virtual Evaluation) was launched in 2022 to reduce the time to diagnosis and increase provider knowledge of genomics in this region, with the goal of improving pediatric health outcomes. We describe our experience of establishing a virtual pediatric genomic service in this region to expeditiously identify, recruit, and evaluate pediatric patients with undiagnosed diseases. METHODS: We have utilized an innovative electronic health record (EHR) agnostic virtual telehealth and educational platform called Consultagene to receive referrals from healthcare providers in the RGV. Using this portal, genetic services, including virtual evaluation and genome sequencing (GS), are being delivered to children with rare diseases. The study has also integrated effective methods to involve and educate community providers through in-person meetings and Continuing Professional Education (CPE) events. RESULTS: The recruitment efforts have proven highly successful with the utilization of Consultagene in this medically underserved region. The project's ongoing engagement efforts with local healthcare providers have resulted in progressively more referrals to the study over time, thus improving inclusion and access to genomic care in the RGV. Additionally, the curated CPE content has been well received by healthcare providers in the region. CONCLUSIONS: Project GIVE study has allowed advanced genetic evaluation and delivery of GS through the virtual Consultagene portal, effectively circumventing the recognized socioeconomic and logistical barriers to accessing genetic services within this border community.

Microevolutionary change in wild stickleback: Using integrative time-series data to infer responses to selection.

A central goal in evolutionary biology is to understand how different evolutionary processes cause trait change in wild populations. However, quantifying evolutionary change in the wild requires linking trait change to shifts in allele frequencies at causal loci. Nevertheless, datasets that allow for such tests are extremely rare and existing theoretical approaches poorly account for the evolutionary dynamics that likely occur in ecological settings. Using a decade-long integrative phenome-to-genome time-series dataset on wild threespine stickleback (Gasterosteus aculeatus), we identified how different modes of selection (directional, episodic, and balancing) drive microevolutionary change in correlated traits over time. Most strikingly, we show that feeding traits changed by as much 25% across 10 generations which was driven by changes in the genetic architecture (i.e., in both genomic breeding values and allele frequencies at genetic loci for feeding traits). Importantly, allele frequencies at genetic loci related to feeding traits changed at a rate greater than expected under drift, suggesting that the observed change was a result of directional selection. Allele frequency dynamics of loci related to swimming traits appeared to be under fluctuating selection evident in periodic population crashes in this system. Our results show that microevolutionary change in a wild population is characterized by different modes of selection acting simultaneously on different traits, which likely has important consequences for the evolution of correlated traits. Our study provides one of the most thorough descriptions to date of how microevolutionary processes result in trait change in a natural population.

Answering open questions in biology using spatial genomics and structured methods.

Genomics methods have uncovered patterns in a range of biological systems, but obscure important aspects of cell behavior: the shapes, relative locations, movement, and interactions of cells in space. Spatial technologies that collect genomic or epigenomic data while preserving spatial information have begun to overcome these limitations. These new data promise a deeper understanding of the factors that affect cellular behavior, and in particular the ability to directly test existing theories about cell state and variation in the context of morphology, location, motility, and signaling that could not be tested before. Rapid advancements in resolution, ease-of-use, and scale of spatial genomics technologies to address these questions also require an updated toolkit of statistical methods with which to interrogate these data. We present a framework to respond to this new avenue of research: four open biological questions that can now be answered using spatial genomics data paired with methods for analysis. We outline spatial data modalities for each open question that may yield specific insights, discuss how conflicting theories may be tested by comparing the data to conceptual models of biological behavior, and highlight statistical and machine learning-based tools that may prove particularly helpful to recover biological understanding.

Seven quick tips for gene-focused computational pangenomic analysis.

Pangenomics is a relatively new scientific field which investigates the union of all the genomes of a clade. The word pan means everything in ancient Greek; the term pangenomics originally regarded genomes of bacteria and was later intended to refer to human genomes as well. Modern bioinformatics offers several tools to analyze pangenomics data, paving the way to an emerging field that we can call computational pangenomics. Current computational power available for the bioinformatics community has made computational pangenomic analyses easy to perform, but this higher accessibility to pangenomics analysis also increases the chances to make mistakes and to produce misleading or inflated results, especially by beginners. To handle this problem, we present here a few quick tips for efficient and correct computational pangenomic analyses with a focus on bacterial pangenomics, by describing common mistakes to avoid and experienced best practices to follow in this field. We believe our recommendations can help the readers perform more robust and sound pangenomic analyses and to generate more reliable results.

PanKA: Leveraging population pangenome to predict antibiotic resistance.

Machine learning has the potential to be a powerful tool in the fight against antimicrobial resistance (AMR), a critical global health issue. Machine learning can identify resistance mechanisms from DNA sequence data without prior knowledge. The first step in building a machine learning model is a feature extraction from sequencing data. Traditional methods like single nucleotide polymorphism (SNP) calling and k-mer counting yield numerous, often redundant features, complicating prediction and analysis. In this paper, we propose PanKA, a method using the pangenome to extract a concise set of relevant features for predicting AMR. PanKA not only enables fast model training and prediction but also improves accuracy. Applied to the Escherichia coli and Klebsiella pneumoniae bacterial species, our model is more accurate than conventional and state-of-the-art methods in predicting AMR.

Parent-of-origin-specific DNA replication timing is confined to large imprinted regions.

Genomic imprinting involves differential DNA methylation and gene expression between homologous paternal and maternal loci. It remains unclear, however, whether DNA replication also shows parent-of-origin-specific patterns at imprinted or other genomic regions. Here, we investigate genome-wide asynchronous DNA replication utilizing uniparental human embryonic stem cells containing either maternal-only (parthenogenetic) or paternal-only (androgenetic) DNA. Four clusters of imprinted genes exhibited differential replication timing based on parent of origin, while the remainder of the genome, 99.82%, showed no significant replication asynchrony between parental origins. Active alleles in imprinted gene clusters replicated earlier than their inactive counterparts. At the Prader-Willi syndrome locus, replication asynchrony spanned virtually the entirety of S phase. Replication asynchrony was carried through differentiation to neuronal precursor cells in a manner consistent with gene expression. This study establishes asynchronous DNA replication as a hallmark of large imprinted gene clusters.

Discovery of therapeutic targets in cancer using chromatin accessibility and transcriptomic data.

Most cancer types lack targeted therapeutic options, and when first-line targeted therapies are available, treatment resistance is a huge challenge. Recent technological advances enable the use of assay for transposase-accessible chromatin with sequencing (ATAC-seq) and RNA sequencing (RNA-seq) on patient tissue in a high-throughput manner. Here, we present a computational approach that leverages these datasets to identify drug targets based on tumor lineage. We constructed gene regulatory networks for 371 patients of 22 cancer types using machine learning approaches trained with three-dimensional genomic data for enhancer-to-promoter contacts. Next, we identified the key transcription factors (TFs) in these networks, which are used to find therapeutic vulnerabilities, by direct targeting of either TFs or the proteins that they interact with. We validated four candidates identified for neuroendocrine, liver, and renal cancers, which have a dismal prognosis with current therapeutic options.

Does nascent polyploidy employ common mechanisms to stabilize and establish?

In a recent issue of Cell Reports, Bray et al. found genetic adaptation in kinetochore components and ion transporters underlying polyploid stabilization in Cochlearia. This resurrects the issue of whether nascent polyploidy in diverse organisms establish via common biological mechanisms.

Where the 'ruber' meets the road: Using the genome of the Red Diamond Rattlesnake to unravel the evolutionary processes driving venom evolution.

Understanding the proximate and ultimate causes of phenotypic variation is fundamental in evolutionary research, as such variation provides the substrate for selection to act upon. Although trait variation can arise due to selection, the importance of neutral processes is sometimes understudied. We presented the first reference-quality genome of the Red Diamond Rattlesnake (Crotalus ruber) and used range-wide 'omic data to estimate the degree to which neutral and adaptive evolutionary processes shaped venom evolution. We characterized population structure and found substantial genetic differentiation across two populations, each with distinct demographic histories. We identified significant differentiation in venom expression across age classes with substantially reduced but discernible differentiation across populations. We then used conditional redundancy analysis to test whether venom expression variation was best predicted by neutral divergence patterns or geographically-variable (a)biotic factors. Snake size was the most significant predictor of venom variation, with environment, prey availability, and neutral sequence variation also identified as significant factors, though to a lesser degree. By directly including neutrality in the model, our results confidently highlight the predominant, yet not singular, role of life history in shaping venom evolution.

Enhancing clinical genomic accuracy with panelGC: a novel metric and tool for quantifying and monitoring GC biases in hybridization capture panel sequencing.

Accurate assessment of fragment abundance within a genome is crucial in clinical genomics applications such as the analysis of copy number variation (CNV). However, this task is often hindered by biased coverage in regions with varying guanine-cytosine (GC) content. These biases are particularly exacerbated in hybridization capture sequencing due to GC effects on probe hybridization and polymerase chain reaction (PCR) amplification efficiency. Such GC content-associated variations can exert a negative impact on the fidelity of CNV calling within hybridization capture panels. In this report, we present panelGC, a novel metric, to quantify and monitor GC biases in hybridization capture sequencing data. We establish the efficacy of panelGC, demonstrating its proficiency in identifying and flagging potential procedural anomalies, even in situations where instrument and experimental monitoring data may not be readily accessible. Validation using real-world datasets demonstrates that panelGC enhances the quality control and reliability of hybridization capture panel sequencing.

Genomes of diverse Actinidia species provide insights into cis-regulatory motifs and genes associated with critical traits.

BACKGROUND: Kiwifruit, belonging to the genus Actinidia, represents a unique fruit crop characterized by its modern cultivars being genetically diverse and exhibiting remarkable variations in morphological traits and adaptability to harsh environments. However, the genetic mechanisms underlying such morphological diversity remain largely elusive. RESULTS: We report the high-quality genomes of five Actinidia species, including Actinidia longicarpa, A. macrosperma, A. polygama, A. reticulata, and A. rufa. Through comparative genomics analyses, we identified three whole genome duplication events shared by the Actinidia genus and uncovered rapidly evolving gene families implicated in the development of characteristic kiwifruit traits, including vitamin C (VC) content and fruit hairiness. A range of structural variations were identified, potentially contributing to the phenotypic diversity in kiwifruit. Notably, phylogenomic analyses revealed 76 cis-regulatory elements within the Actinidia genus, predominantly associated with stress responses, metabolic processes, and development. Among these, five motifs did not exhibit similarity to known plant motifs, suggesting the presence of possible novel cis-regulatory elements in kiwifruit. Construction of a pan-genome encompassing the nine Actinidia species facilitated the identification of gene DTZ79_23g14810 specific to species exhibiting extraordinarily high VC content. Expression of DTZ79_23g14810 is significantly correlated with the dynamics of VC concentration, and its overexpression in the transgenic roots of kiwifruit plants resulted in increased VC content. CONCLUSIONS: Collectively, the genomes and pan-genome of diverse Actinidia species not only enhance our understanding of fruit development but also provide a valuable genomic resource for facilitating the genome-based breeding of kiwifruit.

Are we closer to achieving precision medicine for migraine treatment? A narrative review.

BACKGROUND: The term 'precision medicine' encompasses strategies to optimize diagnosis and outcome prediction and to tailor treatment for individual patients, in consideration of their unique characteristics. The greater availability of multifaceted datasets and strategies to model such data have made precision medicine increasingly possible in recent years. Precision medicine is especially needed in the migraine field since the response to migraine treatments is not universal amongst all individuals with migraine. OBJECTIVE: To provide a narrative review describing contributions to achieving precision medicine for migraine treatment. METHODS: A search of PubMed for English language articles of human participants published from 2005 to January 2024 was conducted to identify articles that reported research contributing to precision medicine for migraine treatment. The published literature was categorized and summarized according to the type of data that were included: clinical phenotypes, genomics, proteomics, physiologic measures, and brain imaging. RESULTS: Published studies have investigated characteristics associated with acute and preventive treatment responses, such as nonsteroidal anti-inflammatory drugs, triptans, onabotulinumtoxinA, and anti-calcitonin gene-related peptide monoclonal antibodies, in patients with episodic or chronic migraine. There is evidence that clinical, genetic, epigenetic, proteomic, physiologic, and brain imaging features might associate with migraine treatment outcomes, although inconsistencies for such findings clearly exist. CONCLUSIONS: The published literature suggests that there are clinical and biological features which associate with, and might be useful for predicting, migraine treatment responses. To achieve precision medicine for migraine treatment, further research is needed that validates and expands on existing findings and tests the accuracy and value of migraine treatment prediction models in clinical settings.